Graph-based term weighting for information retrieval

By Roi Blanco, Christina Lioma. 2012

198 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jamie Callan
2. Zhuyun Dai
3. Konstantinos Skianis
4. Fragkiskos D. Malliaros
5. Michalis Vazirgiannis
6. François Rousseau
7. Luyu Gao
8. Liang Wang

In Statistical and Nonlinear Physics

1. Liang Wang
2. Jamie Callan
3. Michalis Vazirgiannis
4. Zhuyun Dai
5. Konstantinos Skianis
6. Fragkiskos D. Malliaros
7. François Rousseau
8. Shu Wu

Citations in the corpus

• Text Categorization as a Graph Classification Problem (2015, background): “We refer to (Blanco and Lioma, 2012) for an in-depth review of the graph representations of text in NLP.”
• Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval (2019, methodology): “A rich research literature studies how to better estimate term importance (e.g., [2, 3, 5, 18, 27, 36]), however frequency-based signals continue to dominate in both industry and academia.”
• Context-Aware Document Term Weighting for Ad-Hoc Search (2020, methodology): “Graph-based methods were shown effective for short text [4]; but only marginal improvements over standard BM25 were observed on standard document retrieval datasets [4, 33].”
• Graph-based term weighting for text categorization (2015, background): “The pipeline that typically is followed to deal with the problem is similar to the one applied in any classification problem; the goal is to learn the parameters of a classifier from a collection of training documents (with known class information) and then to predict the class of unlabeled documents.”
• Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020, methodology): “The text is preprocessed in a standard way, including tokenisation and stopword removal (Blanco and Lioma, 2012; Rousseau et al., 2015).”
• Graph-of-word and TW-IDF: new approach to ad hoc IR (2013, background): “Initially, we considered undirected edges following related works [5, 16, 2, 3] because an edge captures a co-occurrence of two terms whatever the respective order between them is.”
• Main Core Retention on Graph-of-Words for Single-Document Keyword Extraction (2015, methodology): “The edges were drawn between terms co-occurring within a fixed-size sliding window W of size 4 over the processed text, value consistently reported as working well [3,7,16,19,22].”